End of training

README.md

@@ -17,8 +17,8 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on the None dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.0000
-- Accuracy: 1.0
+- Loss: 0.0096
+- Accuracy: 0.9976
 
 ## Model description
 
@@ -38,28 +38,28 @@ More information needed
 
 The following hyperparameters were used during training:
 - learning_rate: 2e-05
-- train_batch_size: 16
-- eval_batch_size: 16
+- train_batch_size: 32
+- eval_batch_size: 32
 - seed: 42
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
 - num_epochs: 1
-- mixed_precision_training: Native AMP
 
 ### Training results
 
 | Training Loss | Epoch | Step | Validation Loss | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:--------:|
-| 0.0           | 0.09  | 100  | 0.0000          | 1.0      |
-| 0.0           | 0.19  | 200  | 0.0000          | 1.0      |
-| 0.0           | 0.28  | 300  | 0.0000          | 1.0      |
-| 0.0           | 0.37  | 400  | 0.0000          | 1.0      |
-| 0.0           | 0.47  | 500  | 0.0000          | 1.0      |
-| 0.0           | 0.56  | 600  | 0.0000          | 1.0      |
-| 0.0           | 0.65  | 700  | 0.0000          | 1.0      |
-| 0.0           | 0.75  | 800  | 0.0000          | 1.0      |
-| 0.0           | 0.84  | 900  | 0.0000          | 1.0      |
-| 0.0           | 0.93  | 1000 | 0.0000          | 1.0      |
+| 0.1809        | 0.09  | 100  | 0.0608          | 0.9840   |
+| 0.0433        | 0.18  | 200  | 0.0222          | 0.9933   |
+| 0.0248        | 0.27  | 300  | 0.0631          | 0.9834   |
+| 0.0246        | 0.36  | 400  | 0.0363          | 0.9903   |
+| 0.0223        | 0.45  | 500  | 0.0378          | 0.9906   |
+| 0.0172        | 0.53  | 600  | 0.0129          | 0.9969   |
+| 0.0133        | 0.62  | 700  | 0.0208          | 0.9947   |
+| 0.0188        | 0.71  | 800  | 0.0118          | 0.9971   |
+| 0.0134        | 0.8   | 900  | 0.0109          | 0.9971   |
+| 0.0055        | 0.89  | 1000 | 0.0096          | 0.9976   |
+| 0.0055        | 0.98  | 1100 | 0.0096          | 0.9976   |
 
 
 ### Framework versions